Designing Dimensions

Dimensions are fundamental components of a multidimensional database in SQL Server Analysis Services (SSAS). They represent the perspectives or attributes by which you can analyze your business data, such as by time, geography, or product. Effective dimension design is crucial for creating a BI solution that is both performant and intuitive for end-users.

Understanding Dimension Types

Analysis Services supports several types of dimensions, each with its own characteristics and use cases:

• Standard Dimensions: The most common type, representing hierarchical or flat data.
• Junk Dimensions: Used to consolidate low-cardinality, descriptive attributes into a single dimension, reducing the number of dimensions and improving query performance.
• Degenerate Dimensions: Attributes that are not part of a foreign key in a fact table but are still useful for analysis (e.g., invoice number).
• Factless Fact Tables: Used to represent events or relationships without measures.
• Role-Playing Dimensions: A single dimension can be used in multiple ways in a cube (e.g., a 'Date' dimension serving as 'Order Date', 'Ship Date', and 'Delivery Date').

Key Design Considerations

When designing dimensions, consider the following:

• Hierarchy Design: Define meaningful hierarchies that reflect business understanding. This allows for drill-down and roll-up analysis.
• Attribute Relationships: Define relationships between attributes within a dimension. This is vital for query performance and enabling Analysis Services to process the dimension efficiently. Proper attribute relationships ensure that the lowest level attribute accurately determines the members at higher levels.
• Granularity: Determine the lowest level of detail for each dimension. This should align with the granularity of your fact tables.
• Attribute Properties: Configure properties like KeyColumns, NameColumn, and AttributeHierarchyEnabled.
• Member Names: Ensure member names are clear and consistent.
• Performance Optimization: Use techniques like attribute summarization, pre-generated MDX, and appropriate indexing for large dimensions.

Designing Hierarchies

Hierarchies are critical for user navigation and analysis. They can be:

• Natural Hierarchies: Formed by natural relationships in data (e.g., Country > State > City).
• Disjoint Hierarchies: Each level is independent of the others.
• Ragged Hierarchies: Levels within the hierarchy do not have the same depth for all branches.

You can create both user-defined hierarchies (for end-user exploration) and natural (or parent-child) hierarchies.

Attribute Relationships Explained

An attribute relationship defines how one attribute relates to another within the same dimension. This is crucial for performance:

• For Grouping: Attributes that are logically related should have an attribute relationship defined. For example, if 'City' determines 'State', and 'State' determines 'Country', you'd define relationships: City > State, State > Country.
• For Optimization: Analysis Services uses attribute relationships to optimize dimension processing and cube queries. If a relationship is correctly defined, Analysis Services can infer that all members of a lower-level attribute belong to a single member of a higher-level attribute.

Ensure that the relationship is one-to-many (or one-to-one) from the lower level to the higher level. The key column of the higher-level attribute must uniquely identify members of that attribute.

Example: Designing a 'Geography' Dimension

Consider a 'Geography' dimension with attributes like 'Country', 'State', and 'City'.

Data Source View:

-- Assuming a 'DimGeography' table with columns:
-- GeographyKey, CityName, StateProvinceName, CountryName
                

Dimension Design in Analysis Services:

• Key Attribute: GeographyKey (or a combination of keys).
• Attributes: CityName, StateProvinceName, CountryName.
• Attribute Relationships:
  • CityName (Key) > StateProvinceName (Name)
  • StateProvinceName (Key) > CountryName (Name)
• Hierarchies:
  • Natural Hierarchy: Country > State > City

This structure allows users to analyze data by country, state, or city, with drill-down capabilities.

Conclusion

Well-designed dimensions are the backbone of a successful Analysis Services solution. By carefully planning your hierarchies, attribute relationships, and properties, you can build cubes that are efficient, scalable, and provide a rich analytical experience for your users.